{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "synthetic",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python2",
      "display_name": "Python 2"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sK-RhlsGxwpd",
        "colab_type": "text"
      },
      "source": [
        "##### Copyright 2020 Google LLC.\n",
        "\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "you may not use this file except in compliance with the License.\n",
        "You may obtain a copy of the License at\n",
        "\n",
        "    https://www.apache.org/licenses/LICENSE-2.0\n",
        "\n",
        "Unless required by applicable law or agreed to in writing, software\n",
        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "See the License for the specific language governing permissions and\n",
        "limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fuN9k6Gux-yD",
        "colab_type": "text"
      },
      "source": [
        "This colab contains TensorFlow code for implementing the constrained optimization methods presented in the paper:\n",
        "> Harikrishna Narasimhan, Andrew Cotter, Maya Gupta, Serena Wang, \"Pairwise Fairness for Ranking and Regression\", AAAI 2020. [<a href=\"https://arxiv.org/pdf/1906.05330.pdf\">link</a>]\n",
        "\n",
        "First, let's install and import the relevant libraries."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JXgLyAJm0UyB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iTUyZk_A0XnF",
        "colab_type": "text"
      },
      "source": [
        "We will need the TensorFlow Constrained Optimization (TFCO) library."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DvhGP5TW0V_J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install git+https://github.com/google-research/tensorflow_constrained_optimization"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XGFoSFuX0XJc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow_constrained_optimization as tfco"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gBUr48pLzsqK",
        "colab_type": "text"
      },
      "source": [
        "## Constrained Optimization Problem"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Wgq7N73rPHV",
        "colab_type": "text"
      },
      "source": [
        "We will be training a linear ranking model $f(x) = w^\\top x$ where $x \\in \\mathbb{R}^d$ is a set of features for a query-document pair (the protected attribute is not a feature). Our goal is to train the model such that it accurately ranks the positive documents in a query above the negative ones.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6tZrx9BfOB_Q",
        "colab_type": "text"
      },
      "source": [
        "Specifically, for the ranking model $f$, we denote:\n",
        "- $err(f)$ as the pairwise ranking error for model $f$ over all pairs of positive and negative documents\n",
        "$$\n",
        "err(f) = \\mathbf{E}\\big[\\mathbb{I}\\big(f(x) < f(x')\\big) \\,\\big|\\, y = 1,~ y' = 0\\big]\n",
        "$$\n",
        "\n",
        "\n",
        "- $err_{i,j}(f)$ as the pairwise ranking error over positive-negative document pairs where the pos. document is from group $i$, and the neg. document is from group $j$.\n",
        "\n",
        "$$\n",
        "err_{i, j}(f) = \\mathbf{E}\\big[\\mathbb{I}\\big(f(x) < f(x')\\big) \\,\\big|\\, y = 1,~ y' = 0,~ grp(x) = i, ~grp(x') = j\\big]\n",
        "$$\n",
        "<br>\n",
        "\n",
        "We then wish to solve the following constrained problem:\n",
        "$$min_f\\; err(f)$$\n",
        "$$\\text{   s.t.   } |err_{i,j}(f) - err_{k,\\ell}(f)| \\leq \\epsilon \\;\\;\\; \\forall ((i,j), (k,\\ell)) \\in \\mathcal{G},$$\n",
        "\n",
        "where $\\mathcal{G}$ contains the pairs we are interested in constraining."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qM-PzAuykOmN",
        "colab_type": "text"
      },
      "source": [
        "## Generate 2D Synthetic Data\n",
        " \n",
        "We begin by generating a synthetic ranking dataset.\n",
        "\n",
        "**Setting**:\n",
        "- Set of $m$ queries, a set of  $n$ documents associated with each query.\n",
        "- One document per query has a positive label, while the remaining have negative labels. \n",
        "- Each document is  associated with one of two protected groups.\n",
        "\n",
        "\n",
        "**Generative process**:  \n",
        "\n",
        "For convenience, the first document for each query is labeled positive, and remaining $n-1$ documents are labeled negative.\n",
        "\n",
        "For each query-document pair:\n",
        " - Draw the protected group for the document from a $Bernoulli(\\pi)$ distribution\n",
        " - Draw the document features from the following 2D Gaussian distributions:\n",
        "  - $\\mathcal{N}(\\mu_{0,0}, \\Sigma_{0,0})$ if label is -ve and group is 0\n",
        "  - $\\mathcal{N}(\\mu_{1,0}, \\Sigma_{1,0})$ if label is +ve and group is 0\n",
        "  - $\\mathcal{N}(\\mu_{0,1}, \\Sigma_{0,1})$ if label is -ve and group is 1\n",
        "  - $\\mathcal{N}(\\mu_{1,1}, \\Sigma_{1,1})$ if label is +ve and group is 1\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6WvqQsmokWJM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_dataset(num_queries, num_docs):\n",
        "  # Create a synthetic 2-dimensional training dataset with 1000 queries, \n",
        "  # with 1 positive document and 10 negative document each \n",
        "  # and with two protected groups for each document.\n",
        "  num_posdocs = 1\n",
        "  num_negdocs = num_docs - 1\n",
        "  dimension = 2\n",
        "  num_groups = 2\n",
        "  tot_pairs = num_queries * num_posdocs * num_negdocs\n",
        "\n",
        "  # Conditional distributions are Gaussian: Conditioned on the label and the \n",
        "  # protected group, the feature distribution is a Gaussian.\n",
        "  \n",
        "  # Positive documents, Group 0\n",
        "  mu_10 = [1,0]\n",
        "  sigma_10 = np.array([[1, 0], [0, 1]]) \n",
        "\n",
        "  # Positive documents, Group 1\n",
        "  mu_11 = [-1.5, 0.75]\n",
        "  sigma_11 = np.array([[1, 0], [0, 1]]) * 0.5 \n",
        "\n",
        "  # Negative documents, Group 0\n",
        "  mu_00 = [-1,-1]\n",
        "  sigma_00 = np.array([[1, 0], [0, 1]]) \n",
        "\n",
        "  # Negative documents, Group 1\n",
        "  mu_01 = [-2,-1]\n",
        "  sigma_01 = np.array([[1, 0], [0, 1]]) \n",
        "\n",
        "  # Generate positive documents\n",
        "  posdocs_groups = (np.random.rand(num_queries, num_posdocs) <= 0.1) * 1\n",
        "  posdocs_mask = np.dstack([posdocs_groups] * dimension)\n",
        "  posdocs0 = np.random.multivariate_normal(mu_10, sigma_10, \n",
        "                                           size=(num_queries, num_posdocs))\n",
        "  posdocs1 = np.random.multivariate_normal(mu_11, sigma_11, \n",
        "                                           size=(num_queries, num_posdocs))\n",
        "  posdocs = (1 - posdocs_mask) * posdocs0 + posdocs_mask * posdocs1\n",
        "\n",
        "  # Generate negative documents\n",
        "  negdocs_groups = (np.random.rand(num_queries, num_negdocs) <= 0.1) * 1\n",
        "  negdocs_mask = np.dstack([negdocs_groups] * dimension)\n",
        "  negdocs0 = np.random.multivariate_normal(mu_00, sigma_00, \n",
        "                                           size=(num_queries, num_negdocs))\n",
        "  negdocs1 = np.random.multivariate_normal(mu_01, sigma_01, \n",
        "                                           size=(num_queries, num_negdocs))\n",
        "  negdocs = (1 - negdocs_mask) * negdocs0 + negdocs_mask * negdocs1\n",
        "\n",
        "  # Concatenate positive and negative documents for each query \n",
        "  # (along axis 1, where documents are arranged)\n",
        "  features = np.concatenate((posdocs, negdocs), axis=1)\n",
        "\n",
        "  # Concatenate the associated labels:\n",
        "  # (for each query, first num_posdocs documents are positive, remaining negative)\n",
        "  poslabels = np.tile([1], reps=(num_queries, num_posdocs))\n",
        "  neglabels = np.tile([-1], reps=(num_queries, num_negdocs))\n",
        "  labels = np.concatenate((poslabels, neglabels), axis=1)              \n",
        "\n",
        "  # Concatenate the protected groups\n",
        "  groups = np.concatenate((posdocs_groups, negdocs_groups), axis=1)  \n",
        "\n",
        "  dataset = {\n",
        "      'features': features,\n",
        "      'labels': labels,\n",
        "      'groups': groups,\n",
        "      'num_queries': num_queries,\n",
        "      'num_posdocs': num_posdocs,\n",
        "      'num_negdocs': num_negdocs,\n",
        "      'dimension': dimension,\n",
        "      'num_groups': num_groups,\n",
        "      'tot_pairs': tot_pairs\n",
        "  }\n",
        "  return dataset"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m3YfQJ5NzRSE",
        "colab_type": "text"
      },
      "source": [
        "## Plotting Functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lVxfbRjJy41l",
        "colab_type": "text"
      },
      "source": [
        "Next, let's write functions to plot the data and the linear decision boundary."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sRxmpzKV1sPV",
        "colab_type": "code",
        "outputId": "aa727ba2-f37e-491d-b0b8-83c5e35c498e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "def plot_data(dataset, ax=None):\n",
        "  # Plot data set.\n",
        "  features = dataset[\"features\"]\n",
        "  labels = dataset[\"labels\"]\n",
        "  groups = dataset[\"groups\"]\n",
        "  \n",
        "  # Create axes if not specified\n",
        "  if not ax:\n",
        "    _, ax = plt.subplots(1, 1, figsize=(4.0, 4.0))\n",
        "  ax.set_xlabel(\"Feature 0\")\n",
        "  ax.set_ylabel(\"Feature 1\")\n",
        "  \n",
        "  # Plot positive points in group 0\n",
        "  data = features[(labels==1) & (groups==0), :]\n",
        "  ax.plot(data[:,0], data[:,1], 'bx', label=\"Pos, Group 0\")\n",
        "  \n",
        "  # Plot positive points in group 1\n",
        "  data = features[(labels==1) & (groups==1), :]\n",
        "  ax.plot(data[:,0], data[:,1], 'bo', label=\"Pos, Group 1\")\n",
        "  \n",
        "  # Plot negative points in group 0\n",
        "  data = features[(labels==-1) & (groups==0), :]\n",
        "  ax.plot(data[:,0], data[:,1], 'rx', label=\"Neg, Group 0\")\n",
        "  \n",
        "  # Plot negative points in group 1\n",
        "  data = features[(labels==-1) & (groups==1), :]\n",
        "  ax.plot(data[:,0], data[:,1], 'ro', label=\"Neg, Group 1\")\n",
        "  ax.legend(loc = \"upper right\")\n",
        "\n",
        "\n",
        "def plot_model(weights, ax, x_range, y_range, fmt):\n",
        "  # Plot model decision boundary.\n",
        "  ax.plot([x_range[0], x_range[1]], \n",
        "          [-x_range[0] * weights[0] / weights[1],\n",
        "           -x_range[1] * weights[0] / weights[1]],\n",
        "          fmt)\n",
        "  ax.set_ylim(y_range)\n",
        "  \n",
        "\n",
        "# Sample data set.\n",
        "dataset = create_dataset(num_queries=500, num_docs=10)\n",
        "plot_data(dataset)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAARQAAAEKCAYAAADTrKqSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwwTFMIyG8X8uKE09WkDxJwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JxBMPRJA2wvW",
        "colab_type": "text"
      },
      "source": [
        "## Evaluation Metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H4HV7a7wq7rm",
        "colab_type": "text"
      },
      "source": [
        "We will also need functions to evaluate the pairwise error rates for a linear model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K8OQ4ado20p-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_mask(dataset, pos_group, neg_group=None):\n",
        "  # Returns a boolean mask selecting positive-negative document pairs where \n",
        "  # the protected group for  the positive document is pos_group and \n",
        "  # the protected group for the negative document (if specified) is neg_group.\n",
        "  groups = dataset['groups']\n",
        "  num_negdocs = dataset['num_negdocs']\n",
        "  \n",
        "  # Repeat group membership positive docs as many times as negative docs.\n",
        "  mask_pos = groups[:,0] == pos_group\n",
        "  mask_pos_rep = np.repeat(mask_pos.reshape(-1,1), num_negdocs, axis=1)\n",
        "  \n",
        "  if neg_group is None:\n",
        "    return mask_pos_rep\n",
        "  else:\n",
        "    mask_neg = groups[:,1:] == neg_group\n",
        "    return mask_pos_rep & mask_neg\n",
        "\n",
        "\n",
        "def error_rate(model, dataset):\n",
        "  # Returns error rate of model on dataset.\n",
        "  features = dataset[\"features\"]\n",
        "  num_negdocs = dataset['num_negdocs']\n",
        "  \n",
        "  scores = np.matmul(features, model)\n",
        "  pos_scores_rep = np.repeat(scores[:, 0].reshape(-1,1), num_negdocs, axis=1)\n",
        "  neg_scores = scores[:, 1:]\n",
        "  diff = pos_scores_rep - neg_scores\n",
        "  \n",
        "  return np.mean(diff.reshape((-1)) <= 0)\n",
        "\n",
        "\n",
        "def group_error_rate(model, dataset, pos_group, neg_group=None):\n",
        "  # Returns error rate of model on data set, considering only document pairs, \n",
        "  # where the protected group for the positive document is pos_group, and the \n",
        "  # protected group for the negative document (if specified) is neg_group.\n",
        "  features = dataset['features']\n",
        "  num_negdocs = dataset['num_negdocs']\n",
        "  \n",
        "  scores = np.matmul(features, model)\n",
        "  pos_scores_rep = np.repeat(scores[:, 0].reshape(-1,1), num_negdocs, axis=1)\n",
        "  neg_scores = scores[:, 1:]\n",
        "  \n",
        "  mask = get_mask(dataset, pos_group, neg_group)\n",
        "  diff = pos_scores_rep - neg_scores\n",
        "  diff = diff * mask\n",
        "   \n",
        "  return np.sum(diff.reshape((-1)) < 0) * 1.0 / np.sum(mask)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kI8xNJDcpQYP",
        "colab_type": "text"
      },
      "source": [
        "## Create Linear Model\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lY4hvJAOra6s",
        "colab_type": "text"
      },
      "source": [
        "We then write a function to create the linear ranking model in Tensorflow."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eTQOebAepXSu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def group_tensors(predictions, dataset, pos_group, neg_group=None):\n",
        "  # Returns predictions and labels for document-pairs where the protected group \n",
        "  # for the positive document is pos_group, and the protected group for the \n",
        "  # negative document (if specified) is neg_group.\n",
        "  mask = get_mask(dataset, pos_group, neg_group)\n",
        "  mask = np.reshape(mask, (-1))\n",
        "  group_labels = lambda: tf.constant(np.ones(np.sum(mask)), dtype=tf.float32)\n",
        "  group_predictions = lambda: tf.boolean_mask(predictions(), mask)\n",
        "  return group_predictions, group_labels\n",
        "\n",
        "\n",
        "def linear_model(dataset):\n",
        "  # Creates a linear ranking model, and returns a nullary function returning\n",
        "  # predictions on the dataset, and the model weights.\n",
        "\n",
        "  # Create variables containing the model parameters.\n",
        "  weights = tf.Variable(tf.ones(dataset[\"dimension\"], dtype=tf.float32), \n",
        "                        name=\"weights\")\n",
        "\n",
        "  # Create a constant tensor containing the features.\n",
        "  features_tensor = tf.constant(dataset[\"features\"], dtype=tf.float32)\n",
        "\n",
        "  # Create a nullary function that returns applies the linear model to the \n",
        "  # features and returns the tensor with the predictions.\n",
        "  def predictions():\n",
        "    predicted_scores = tf.tensordot(features_tensor, weights, axes=(2, 0))\n",
        "\n",
        "    # Compute ranking errors and flatten tensor.\n",
        "    pos_scores = tf.slice(predicted_scores, begin=[0,0], \n",
        "                          size=[-1, dataset[\"num_posdocs\"]])\n",
        "    neg_scores = tf.slice(predicted_scores, begin=[0, dataset[\"num_posdocs\"]], \n",
        "                          size=[-1,-1])\n",
        "    pos_scores_rep = tf.tile(pos_scores, multiples=(1, dataset[\"num_negdocs\"]))\n",
        "    predictions_tensor = tf.reshape(pos_scores_rep - neg_scores, shape=[-1])\n",
        "\n",
        "    return predictions_tensor\n",
        "  \n",
        "  return predictions, weights"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WIBvG3Arv7zR",
        "colab_type": "text"
      },
      "source": [
        "## Formulate Optimization Problem"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SfZd-XPt0A8E",
        "colab_type": "text"
      },
      "source": [
        "We are ready to formulate the constrained optimization problem using the TFCO library."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0AfVknixv9So",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def formulate_problem(dataset, constraint_groups=[], constraint_slack=None):\n",
        "  # Returns a RateMinimizationProblem object and the linear model weights.\n",
        "  #\n",
        "  # Formulates a constrained problem that optimizes the error rate for a linear\n",
        "  # model on the specified dataset, subject to pairwise fairness constraints \n",
        "  # specified by the constraint_groups and the constraint_slack.\n",
        "  # \n",
        "  # Args:\n",
        "  #   dataset: Dataset dictionary returned by create_dataset()\n",
        "  #   constraint_groups: List containing tuples of the form \n",
        "  #     ((pos_group0, neg_group0), (pos_group1, neg_group1)), specifying the \n",
        "  #     group memberships for the document pairs to compare in the constraints.\n",
        "  #   constraint_slack: slackness \"\\epsilon\" allowed in the constraints.\n",
        "\n",
        "  # Create linear model: we get back a nullary function returning the \n",
        "  # predictions on the dataset, and the model weights.\n",
        "  predictions, weights = linear_model(dataset)\n",
        "\n",
        "  # Create a nullary function returning a constant tensor with the labels.\n",
        "  labels = lambda: tf.constant(np.ones(dataset[\"tot_pairs\"]), dtype=tf.float32)\n",
        "  \n",
        "  # Context for the optimization objective.\n",
        "  context = tfco.rate_context(predictions, labels)\n",
        "  \n",
        "  # Constraint set.\n",
        "  constraint_set = []\n",
        "  \n",
        "  # Context for the constraints.\n",
        "  for ((pos_group0, neg_group0), (pos_group1, neg_group1)) in constraint_groups:\n",
        "    # Context for group 0.\n",
        "    group0_predictions, group0_labels = group_tensors(\n",
        "        predictions, dataset, pos_group0, neg_group0)\n",
        "    context_group0 = tfco.rate_context(group0_predictions, group0_labels)\n",
        "    # Context for group 1.\n",
        "    group1_predictions, group1_labels = group_tensors(\n",
        "        predictions, dataset, pos_group1, neg_group1)\n",
        "    context_group1 = tfco.rate_context(group1_predictions, group1_labels)\n",
        "\n",
        "    # Add constraints to constraint set.\n",
        "    constraint_set.append(\n",
        "        tfco.false_negative_rate(context_group0) <= (\n",
        "            tfco.false_negative_rate(context_group1) + constraint_slack)\n",
        "    )\n",
        "    constraint_set.append(\n",
        "        tfco.false_negative_rate(context_group1) <= (\n",
        "            tfco.false_negative_rate(context_group0) + constraint_slack)\n",
        "    )\n",
        "  \n",
        "  # Formulate constrained minimization problem.\n",
        "  problem = tfco.RateMinimizationProblem(\n",
        "      tfco.error_rate(context), constraint_set)\n",
        "  \n",
        "  return problem, weights"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P1x4yEllRKjH",
        "colab_type": "text"
      },
      "source": [
        "## Train Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "16nddoPIrmuj",
        "colab_type": "text"
      },
      "source": [
        "The following function then trains the linear model by solving the above constrained optimization problem. We will handle three type of pairwise fairness constraints."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TWKxylqXPbR4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Pairwise fairness constraint types.\n",
        "MARGINAL_EQUAL_OPPORTUNITY = 0\n",
        "CROSS_GROUP_EQUAL_OPPORTUNITY = 1\n",
        "CROSS_AND_WITHIN_GROUP_EQUAL_OPPORTUNITY = 2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Md5pDHyBRN83",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_model(train_set, params):\n",
        "  # Trains the model and returns the trained model weights, objective and \n",
        "  # maximum constraint violation values.\n",
        "\n",
        "  # Set up problem and model.\n",
        "  if params['flag_constrained']:\n",
        "    # Constrained optimization.\n",
        "    if params['constraint_type'] == MARGINAL_EQUAL_OPPORTUNITY:\n",
        "      constraint_groups = [((0, None), (1, None))]\n",
        "    elif params['constraint_type'] == CROSS_GROUP_EQUAL_OPPORTUNITY:\n",
        "      constraint_groups = [((0, 1), (1, 0))]\n",
        "    else:\n",
        "      constraint_groups = [((0, 1), (1, 0)), ((0, 0), (1, 1))]\n",
        "  else:\n",
        "    # Unconstrained optimization.\n",
        "    constraint_groups = []\n",
        "\n",
        "  problem, weights = formulate_problem(\n",
        "      train_set, constraint_groups, params[\"constraint_bound\"])\n",
        "  \n",
        "  # Set up optimization problem.\n",
        "  optimizer = tfco.ProxyLagrangianOptimizerV2(\n",
        "      tf.keras.optimizers.Adagrad(learning_rate=params[\"learning_rate\"]),\n",
        "      num_constraints=problem.num_constraints)\n",
        "  \n",
        "  # List of trainable variables.\n",
        "  var_list = (\n",
        "      [weights] + problem.trainable_variables + optimizer.trainable_variables())\n",
        "  \n",
        "  # List of objectives and constraint violations during course of training.\n",
        "  objectives = []\n",
        "  constraints = []\n",
        "\n",
        "  # Run loops * iterations_per_loop full batch iterations.\n",
        "  for ii in xrange(params['loops']):\n",
        "    for ii in xrange(params['iterations_per_loop']):\n",
        "      optimizer.minimize(problem, var_list=var_list)\n",
        "    objectives.append(problem.objective())\n",
        "    if params['flag_constrained']:\n",
        "      constraints.append(max(problem.constraints()))\n",
        "\n",
        "  return weights.numpy(), objectives, constraints"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WxFJV0tvKvyR",
        "colab_type": "text"
      },
      "source": [
        "## Summarize and Plot Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i7In7Ra7M_S7",
        "colab_type": "text"
      },
      "source": [
        "Having trained a model, we will need functions to summarize the various evaluation metrics and plot the trained decision boundary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CBl5KfEOPApl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def evaluate_results(model, test_set, params):\n",
        "  # Returns overall and group error rates for model on test set.\n",
        "  if params['constraint_type'] == MARGINAL_EQUAL_OPPORTUNITY:\n",
        "    return (error_rate(model, test_set),\n",
        "            [group_error_rate(model, test_set, 0), \n",
        "             group_error_rate(model, test_set, 1)])\n",
        "  else:\n",
        "    return (error_rate(model, test_set),\n",
        "            [[group_error_rate(model, test_set, 0, 0),\n",
        "              group_error_rate(model, test_set, 0, 1)],\n",
        "             [group_error_rate(model, test_set, 1, 0),\n",
        "              group_error_rate(model, test_set, 1, 1)]])\n",
        "    \n",
        "    \n",
        "def display_results(model, objectives, constraints, test_set, params, ax):\n",
        "  # Prints evaluation results for model on test data \n",
        "  # and plots its decision boundary\n",
        "  \n",
        "  # Evaluate model on test set and print results.\n",
        "  error, group_error = evaluate_results(model, test_set, params)\n",
        "  \n",
        "  if params['constraint_type'] == MARGINAL_EQUAL_OPPORTUNITY:\n",
        "    if params['flag_constrained']:\n",
        "      print 'Constrained', '\\t \\t',\n",
        "    else:\n",
        "      print 'Test Error\\t \\t', 'Overall', '\\t', 'Group 0', '\\t', 'Group 1', \n",
        "      print '\\t', 'Diff'\n",
        "      print 'Unconstrained', '\\t \\t',\n",
        "    print(\"%.3f\" % (error_rate(model, test_set)) + '\\t\\t' +\n",
        "          \"%.3f\" % group_error[0] + '\\t\\t' + \"%.3f\" % group_error[1] + '\\t\\t'\n",
        "          \"%.3f\" % abs(group_error[0] - group_error[1]))\n",
        "  elif params['constraint_type'] == CROSS_GROUP_EQUAL_OPPORTUNITY:\n",
        "    if params['flag_constrained']:\n",
        "      print 'Constrained', '\\t \\t',\n",
        "    else:\n",
        "      print 'Test Error\\t \\t', 'Overall', '\\t', 'Group 0/1', '\\t', 'Group 1/0',\n",
        "      print '\\t', 'Diff'\n",
        "      print 'Unconstrained', '\\t \\t',\n",
        "    print(\"%.3f\" % error + '\\t\\t' + \n",
        "          \"%.3f\" % group_error[0][1] + '\\t\\t' +\n",
        "          \"%.3f\" % group_error[1][0] + '\\t\\t' +\n",
        "          \"%.3f\" % abs(group_error[0][1] - group_error[1][0]))\n",
        "  else:\n",
        "    if params['flag_constrained']:\n",
        "      print 'Constrained', '\\t \\t',\n",
        "    else:\n",
        "      print 'Test Error\\t \\t', 'Overall', '\\t', 'Group 0/1', '\\t', 'Group 1/0',\n",
        "      print '\\t', 'Diff', '\\t',\n",
        "      print '\\t', 'Group 0/0', '\\t', 'Group 1/1', '\\t', 'Diff'\n",
        "      print 'Unconstrained', '\\t \\t',\n",
        "    print(\"%.3f\" % error + '\\t\\t' + \n",
        "          \"%.3f\" % group_error[0][1] + '\\t\\t' +\n",
        "          \"%.3f\" % group_error[1][0] + '\\t\\t' +\n",
        "          \"%.3f\" % abs(group_error[0][1] - group_error[1][0]) + '\\t\\t' +\n",
        "          \"%.3f\" % group_error[0][0] + '\\t\\t' +\n",
        "          \"%.3f\" % group_error[1][1] + '\\t\\t' +\n",
        "          \"%.3f\" % abs(group_error[0][0] - group_error[1][1]))\n",
        "  \n",
        "  # Plot decision boundary and progress of training objective/constraint viol.\n",
        "  if params['flag_constrained']:\n",
        "    ax[0].set_title(\"Model: Constrained\")\n",
        "  else:\n",
        "    ax[0].set_title(\"Model: Unconstrained\")\n",
        "  features = train_set['features']\n",
        "  plot_data(train_set, ax[0])\n",
        "  plot_model(model, ax[0], \n",
        "             [features[:, :, 0].min(), features[:, :, 0].max()], \n",
        "             [features[:, :, 1].min(), features[:, :, 1].max()], \n",
        "             \"k--\")\n",
        "  \n",
        "  if params['flag_constrained']:\n",
        "    ax[1].set_title(\"Objective (Hinge)\")\n",
        "    ax[1].set_xlabel(\"Number of epochs\")\n",
        "    objective_curve, = ax[1].plot(range(params['loops']), objectives)\n",
        "\n",
        "    ax[2].set_title(\"Constraint Violation\")\n",
        "    ax[2].set_xlabel(\"Number of epochs\")\n",
        "    constraint_curve, = ax[2].plot(range(params['loops']), constraints)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PQR0nnORRedG",
        "colab_type": "text"
      },
      "source": [
        "# Experimental Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jTYOW_EOsrWV",
        "colab_type": "text"
      },
      "source": [
        "We now run experiments with two types of pairwise fairness criteria: (1) marginal equal opportunity and (2) pairwise equal opportunity. In each case, we compare an unconstrained model trained to optimize the error rate and a constrained model trained with pairwise fairness constraints."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jqxzaPTEwEIn",
        "colab_type": "text"
      },
      "source": [
        "## (1) Marginal Equal Opportunity\n",
        "\n",
        "\n",
        "For a ranking model $f: \\mathbb{R}^d \\rightarrow \\mathbb{R}$, recall:\n",
        "- $err(f)$ as the pairwise ranking error for model $f$ over all pairs of positive and negative documents\n",
        "$$\n",
        "err(f) ~=~ \\mathbf{E}\\big[\\mathbb{I}\\big(f(x) < f(x')\\big) \\,\\big|\\, y = 1,~ y' = 0\\big]\n",
        "$$\n",
        "\n",
        "and we additionally define:\n",
        "\n",
        "- $err_i(f)$ as the row-marginal pairwise error over positive-negative document pairs where the pos. document is from group $i$, and the neg. document is from either groups\n",
        "\n",
        "$$\n",
        "err_i(f) = \\mathbf{E}\\big[\\mathbb{I}\\big(f(x) < f(x')\\big) \\,\\big|\\, y = 1,~ y' = 0,~ grp(x) = i\\big]\n",
        "$$\n",
        "\n",
        "The constrained optimization problem we solve constraints the row-marginal pairwise errors to be similar:\n",
        "\n",
        "$$min_f\\;err(f)$$\n",
        "\n",
        "$$\\text{s.t.   }\\;|err_0(f) - err_1(f)| \\leq 0.01$$\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2iboCFdiHV4r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "outputId": "9577981e-2c99-4b08-d351-ed3223456c89"
      },
      "source": [
        "np.random.seed(121212)\n",
        "train_set = create_dataset(num_queries=500, num_docs=10)\n",
        "test_set = create_dataset(num_queries=500, num_docs=10)\n",
        "\n",
        "model_params = {\n",
        "    'loops': 25, \n",
        "    'iterations_per_loop': 10, \n",
        "    'learning_rate': 1.0,\n",
        "    'constraint_type': MARGINAL_EQUAL_OPPORTUNITY, \n",
        "    'constraint_bound': 0.01}\n",
        "\n",
        "# Plot training stats, data and model.\n",
        "ff, ax = plt.subplots(1, 4, figsize=(16.0, 3.5))\n",
        "\n",
        "# Unconstrained optimization.\n",
        "model_params['flag_constrained'] = False\n",
        "model, objectives, constraints = train_model(train_set, model_params)\n",
        "display_results(model, objectives, constraints, test_set, model_params, [ax[0]])\n",
        "\n",
        "# Constrained optimization.\n",
        "model_params['flag_constrained'] = True\n",
        "model, objectives, constraints = train_model(train_set, model_params)\n",
        "display_results(model, objectives, constraints, test_set, model_params, ax[1:])\n",
        "\n",
        "ff.tight_layout()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Error\t \tOverall \tGroup 0 \tGroup 1 \tDiff\n",
            "Unconstrained \t \t0.077\t\t0.053\t\t0.257\t\t0.203\n",
            "Constrained \t \t0.095\t\t0.092\t\t0.113\t\t0.021\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABHgAAAD0CAYAAADgz5HzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
            "text/plain": [
              "<Figure size 1152x252 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "CorY2URkop1Y"
      },
      "source": [
        "## (2) Pairwise Equal Opportunity\n",
        "\n",
        "Recall that we denote\n",
        " $err_{i,j}(f)$ as the ranking error over positive-negative document pairs where the pos. document is from group $i$, and the neg. document is from group $j$.\n",
        "$$\n",
        "err_{i, j}(f) ~=~ \\mathbf{E}\\big[\\mathbb{I}\\big(f(x) < f(x')\\big) \\,\\big|\\, y = 1,~ y' = 0,~ grp(x) = i, ~grp(x') = j\\big]\n",
        "$$\n",
        "\n",
        "\n",
        "We first constrain only the cross-group errors, highlighted below.\n",
        "\n",
        "<br>\n",
        "<table border=\"1\" bordercolor=\"black\">\n",
        "  <tr >\n",
        "     <td bgcolor=\"white\"> </td>\n",
        "     <td bgcolor=\"white\"> </td>\n",
        "     <td bgcolor=\"white\"  colspan=2 align=center><b>Negative</b></td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td bgcolor=\"white\"></td>\n",
        "    <td bgcolor=\"white\"></td>\n",
        "    <td>Group 0</td>\n",
        "    <td>Group 1</td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td bgcolor=\"white\" rowspan=2><b>Positive</b></td>\n",
        "    <td bgcolor=\"white\">Group 0</td>\n",
        "    <td bgcolor=\"white\">$err_{0,0}$</td>\n",
        "    <td bgcolor=\"white\">$\\mathbf{err_{0,1}}$</td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>Group 1</td>\n",
        "     <td bgcolor=\"white\">$\\mathbf{err_{1,0}}$</td>\n",
        "      <td bgcolor=\"white\">$err_{1,1}$</td>\n",
        "  </tr>\n",
        "</table>\n",
        "<br>\n",
        "\n",
        "The optimization problem we solve constraints the cross-group pairwise errors to be similar:\n",
        "\n",
        "$$min_f\\; err(f)$$\n",
        "$$\\text{s.t. }\\;\\; |err_{0,1}(f) - err_{1,0}(f)| \\leq 0.01$$\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ejzFg8McNswG",
        "colab_type": "code",
        "outputId": "0f62f733-7c86-445d-c1ec-3199df446cd4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        }
      },
      "source": [
        "model_params = {\n",
        "    'loops': 50, \n",
        "    'iterations_per_loop': 10, \n",
        "    'learning_rate': 1.0, \n",
        "    'constraint_type': CROSS_GROUP_EQUAL_OPPORTUNITY, \n",
        "    'constraint_bound': 0.01}\n",
        "\n",
        "# Plot training stats, data and model\n",
        "ff, ax = plt.subplots(1, 4, figsize=(16.0, 3.5))\n",
        "\n",
        "model_params['flag_constrained'] = False\n",
        "model, objectives, constraints = train_model(train_set, model_params)\n",
        "display_results(model, objectives, constraints, test_set, model_params, [ax[0]])\n",
        "\n",
        "model_params['flag_constrained'] = True\n",
        "model, objectives, constraints = train_model(train_set, model_params)\n",
        "display_results(model, objectives, constraints, test_set, model_params, ax[1:])\n",
        "\n",
        "ff.tight_layout()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Error\t \tOverall \tGroup 0/1 \tGroup 1/0 \tDiff\n",
            "Unconstrained \t \t0.077\t\t0.013\t\t0.277\t\t0.263\n",
            "Constrained \t \t0.118\t\t0.091\t\t0.097\t\t0.005\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABHgAAAD0CAYAAADgz5HzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMzOzkvsLmXSp50zR/9QAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x252 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q23bS58DxPsm",
        "colab_type": "text"
      },
      "source": [
        "### Cross-group + Within-group Comparisons\n",
        "We next constrain both the cross-group errors to be similar, and the within-group errors to be similar:\n",
        "<br>\n",
        "<table border=\"1\" bordercolor=\"black\">\n",
        "  <tr >\n",
        "     <td bgcolor=\"white\"> </td>\n",
        "     <td bgcolor=\"white\"> </td>\n",
        "     <td bgcolor=\"white\"  colspan=2 align=center><b>Negative</b></td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td bgcolor=\"white\"></td>\n",
        "    <td bgcolor=\"white\"></td>\n",
        "    <td>Group 0</td>\n",
        "    <td>Group 1</td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td bgcolor=\"white\" rowspan=2><b>Positive</b></td>\n",
        "    <td bgcolor=\"white\">Group 0</td>\n",
        "    <td bgcolor=\"white\">$\\mathbf{err_{0,0}}$</td>\n",
        "    <td bgcolor=\"white\">$\\mathbf{err_{0,1}}$</td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>Group 1</td>\n",
        "     <td bgcolor=\"white\">$\\mathbf{err_{1,0}}$</td>\n",
        "      <td bgcolor=\"white\">$\\mathbf{err_{1,1}}$</td>\n",
        "  </tr>\n",
        "</table>\n",
        "<br>\n",
        "\n",
        "The constrained optimization problem we wish to solve is given by:\n",
        "$$min_f \\;err(f)$$\n",
        "$$\\text{s.t.  }\\;\\;|err_{0,1}(f) - err_{1,0}(f)| \\leq 0.01\n",
        "\\;\\;\\; |err_{0,0}(f) - err_{1,1}(f)| \\leq 0.01\n",
        "$$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "fd2ca829-9c3f-4672-9417-372e48c2f6b7",
        "id": "OMXzmOLpqexb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        }
      },
      "source": [
        "model_params = {\n",
        "    'loops': 250, \n",
        "    'iterations_per_loop': 10, \n",
        "    'learning_rate': 0.2, \n",
        "    'constraint_type': CROSS_AND_WITHIN_GROUP_EQUAL_OPPORTUNITY, \n",
        "    'constraint_bound': 0.01\n",
        "}\n",
        "\n",
        "# Plot training stats, data and model\n",
        "ff, ax = plt.subplots(1, 4, figsize=(16.0, 3.5))\n",
        "\n",
        "model_params['flag_constrained'] = False\n",
        "model, objectives, constraints = train_model(train_set, model_params)\n",
        "display_results(model, objectives, constraints, test_set, model_params, [ax[0]])\n",
        "\n",
        "model_params['flag_constrained'] = True\n",
        "model, objectives, constraints = train_model(train_set, model_params)\n",
        "display_results(model, objectives, constraints, test_set, model_params, ax[1:])\n",
        "\n",
        "ff.tight_layout()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Error\t \tOverall \tGroup 0/1 \tGroup 1/0 \tDiff \t\tGroup 0/0 \tGroup 1/1 \tDiff\n",
            "Unconstrained \t \t0.077\t\t0.013\t\t0.277\t\t0.263\t\t0.057\t\t0.089\t\t0.032\n",
            "Constrained \t \t0.099\t\t0.054\t\t0.116\t\t0.062\t\t0.103\t\t0.054\t\t0.049\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABHgAAAD0CAYAAADgz5HzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZmZmljlP8JiZmZmZmZmZZe6/Wnq7etavJmQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x252 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tg9uNCwiFP3T",
        "colab_type": "text"
      },
      "source": [
        "### Conclusion\n",
        "The experimental results show that unconstrained training performs poorly on the pairwise fairness metrics, while the constrained optimization methods perform yield significantly lower fairness violations at the cost of a higher overall error rate. The constrained model ends up substatially lowering the error rates associated with the minorty group (group 1), at the cost of a slightly higher error rate for the majority group (group 0).\n",
        "\n",
        "Also, note that the quality of the learned ranking function depends on the slope of the hyperplane and is unaffected by its intercept. The hyperplane learned by the unconstrained approach ranks the majority examples (the group marked with x) well, but is not accurate at ranking the minority examples (the group marked with o). "
      ]
    }
  ]
}